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Model Comparison

Nemotron 3.5 Super VL vs Reka EdgeWhich Is Better in 2026?

NVIDIA: Nemotron 3.5 Super VL logoNVIDIA•
Reka AI

Nemotron 3.5 Super VL vs Reka Edge: which should you choose in 2026?

Nemotron 3.5 Super VL (by NVIDIA) and Reka Edge (by Reka AI) are compared below. Here is how they stack up on benchmarks, price, and capabilities, and which one to pick in 2026.

There are not enough shared, protocol-compatible benchmark results to declare a performance leader.

Reka Edge is about 2.4× cheaper on a blended 3:1 input/output basis ($0.10 vs $0.2375 per 1M tokens).

Nemotron 3.5 Super VL has the larger context window (1,048,576 tokens vs 16,384 tokens).

Choose Nemotron 3.5 Super VL if…

  • • you work with longer documents, transcripts, or codebases

Choose Reka Edge if…

  • • lower blended API cost matters for your workload

The benchmark count includes only results measured with a matching benchmark version and protocol. Arena scores are shown separately. Missing, preliminary, and incompatible data is not treated as a controlled win. Published point-score comparisons are labeled separately when protocol details are incomplete. For text-output models, the verdict also compares token pricing and context windows.

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Performance benchmarks

Every value links to its source. A dash means that no reviewed result is available for that exact model and protocol.

Nemotron 3.5 Super VL and Reka Edge benchmark results
BenchmarkNemotron 3.5 Super VLReka Edge
AutomationBench-AA

A 657-task benchmark of multi-step work across simulated SaaS applications in six business domains. The Artificial Analysis protocol reports a guardrail-aware score and is distinct from both the public Zapier split and the unrelated dynamic AutoBench framework.

Guardrail-aware score

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BrowseComp

A benchmark of difficult, verifiable information-seeking questions designed to measure an agent's ability to locate hard-to-find facts through web browsing.

Accuracy

——
OSWorld-Verified

A verified computer-use benchmark in which multimodal agents operate desktop applications and are graded from the resulting environment state.

Mean task reward

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SWE-Bench Verified

A human-validated subset of real GitHub issues used to measure whether a coding agent can produce repository patches that resolve the associated tests.

Resolved

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Terminal-Bench 2.1

Version 2.1 of the benchmark for completing realistic tasks in terminal environments. Harness, resource limits, and attempt count are part of the protocol.

Mean task success

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AutoBench

A dynamic LLM evaluation framework in which models generate questions, answer them, and participate in reciprocal peer assessment. AutoBench is distinct from Zapier's AutomationBench.

Weighted peer-assessment score

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GPQA Diamond

The highest-quality subset of Graduate-Level Google-Proof Q&A, designed to test expert-level scientific reasoning in biology, physics, and chemistry.

Accuracy

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Humanity's Last Exam

A 2,500-question expert-level benchmark spanning dozens of academic fields. Tool-assisted and no-tools results are separate protocols and must not be merged.

Accuracy

——

Benchmark sources

    Reviewed evidence last updated 2026-08-09.

    Pricing, capabilities, and model facts

    Nemotron 3.5 Super VL and Reka Edge model facts
    FeatureNemotron 3.5 Super VLReka Edge
    Context & model facts
    DeveloperNVIDIAReka AI
    API providerNVIDIAReka Edge
    Input context1,048,576 tokens16,384 tokens
    Maximum output235,929 tokens14,745 tokens
    Released——
    Added to WritingmateOct 15, 2026Mar 20, 2026
    LicenseNot availableNot available
    Knowledge cutoff——
    Capabilities
    InputsText, Image, VideoImage, Text, Video
    OutputsTextText
    Provider endpoint accepts tool parametersYesYes
    ReasoningYesYes
    VisionYesYes
    Image GenerationNoNo
    Video GenerationNoNo
    API pricing
    Input (per 1M tokens)$0.15$0.10
    Output (per 1M tokens)$0.50$0.10
    Blended 3:1 input/output$0.2375$0.10
    API performance
    p95 latencyNot measuredNot measured
    Output throughputNot measuredNot measured
    Writingmate shows API performance only when both models have enough observations from the same measurement window, prompt profile, and provider. Third-party latency values are not copied into this table.

    Data sources

    • Writingmate model catalog (pricing, limits, and availability)

    Catalog data last updated Oct 15, 2026.

    Why Pay for Multiple Subscriptions?

    Comparing Nemotron 3.5 Super VL from NVIDIA with Reka Edge from Reka AI? Instead of managing separate API keys and subscriptions, get both with Writingmate.

    Subscription-plan access for Nemotron 3.5 Super VL and Reka Edge
    PlanPriceNemotron 3.5 Super VLReka EdgeAI ImagesAI Video
    Writingmate Pro
    Most popular
    		$20/moIncludedIncludedNano Banana Pro, FLUX.2, DALL-E & moreVEO 3.1, Kling 3.0
    Writingmate Ultimate
    Power users
    		$60/moIncludedIncludedNano Banana Pro, FLUX.2, DALL-E & moreVEO 3.1, Kling 3.0
    View Plans & Pricing

    Nemotron 3.5 Super VL vs Reka Edge FAQ

    Which is better, Nemotron 3.5 Super VL or Reka Edge?

    There are not enough shared, protocol-compatible benchmark results to declare a performance leader. Reka Edge is about 2.4× cheaper on a blended 3:1 input/output basis ($0.10 vs $0.2375 per 1M tokens). Nemotron 3.5 Super VL has the larger context window (1,048,576 tokens vs 16,384 tokens).

    Which model is cheaper to use through an API?

    Reka Edge is about 2.4× cheaper on a blended 3:1 input/output basis ($0.10 vs $0.2375 per 1M tokens).

    Which model supports more context?

    Nemotron 3.5 Super VL has the larger context window (1,048,576 tokens vs 16,384 tokens).

    Can I switch between Nemotron 3.5 Super VL and Reka Edge?

    Yes. Use the model selector on this page to open any current Writingmate model comparison. You can also run the same prompt with both models in Writingmate.